Many models attempt to estimate music rhythmic parameters separately e.g., [1]}, [2]}, [3]} . However, due to the underlying inter-dependencies among these parameters, joint approaches have demonstrated promising results as well [4]}, [5]}, [6]}. Joint models often employ state spaces to model these inter-dependencies, and use some probabilistic models such as Bayesian models to infer the parameters of interest. As rhythmic parameters are intrinsically continuous variables, the state spaces are ideally continuous or discretized with a fine granularity. Hence, the inference can be computationally expensive when the state spaces are high dimensional.
